Skip to main content

The Shifting Network: Volume Signalling in Real and Robot Nervous Systems

  • Conference paper
  • First Online:
Advances in Artificial Life (ECAL 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2159))

Included in the following conference series:

Abstract

This paper presents recent work in computational modelling of diffusing gaseous neuromodulators in biological nervous systems. It goes on to describe work in adaptive autonomous systems directly inspired by this: an exploration of the use of virtual diffusing modulators in robot nervous systems built from non-standard artificial neural networks. These virtual chemicals act over space and time modulating a variety of node and connection properties in the networks. A wide variety of rich dynamics are possible in such systems; in the work described here, evolutionary robotics techniques have been used to harness the dynamics to produce autonomous behaviour in mobile robots. Detailed comparative analyses of evolutionary searches, and search spaces, for robot controllers with and without the virtual gases are introduced. The virtual diffusing modulators are found to provide significant advantages.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bredt DS and Snyder SH (1990) Isolation of nitric oxide synthetase, a calmodulin-requiring enzyme. Proc Natl Acad Sci USA 87: 682–685.

    Article  Google Scholar 

  2. Brooks, R.A. (1994) Coherent Behavior from Many Adaptive Processes. In: D. Cliff and P. Husbands and J.-A. Meyer and S.W. Wilson (Eds.), From Animals to Animats 3: Proceedings of The Third International Conference on Simulation of Adaptive Behavior, 22–29, MIT Press/Bradford Books, Cambridge, MA.

    Google Scholar 

  3. Cavalli-Sforza, L. and Feldman, M. (1976). Evolution of continuous variation: Direct approaches through joint distribution of genotypes and phenotypes. Proc. Nat. Academy of Sciences, USA, 73:1689–1692.

    Article  MATH  Google Scholar 

  4. Collins, R. and Jefferson, D. (1991) Selection in massively parallel genetic algorithms. In: R. K. Belew and L. B. Booker (Eds), Proceedings of the Fourth Intl. Conf. on Genetic Algorithms, ICGA-91, 249–256, Morgan Kaufmann.

    Google Scholar 

  5. Gally JA, Montague PR, Reeke Jnr GN and Edelman GM (1990) The NO hypothesis: possible effects of a short-lived, rapidly diffusible signal in the development and function of the nervous system. Proc Natl AcadSci USA, 87:3547–3551.

    Article  Google Scholar 

  6. Garthwaite J, Charles SL and Chess-Williams R (1988) Endothelium-derived relaxing factor release on activation of NMDA receptors suggests role as intracellular messenger in the brain. Nature 336: 385–388.

    Article  Google Scholar 

  7. Grand, S. Creatures: An exercise in Creation, IEEE Intelligent Systems magazine, July/August 1997.

    Google Scholar 

  8. Hall ZW (1992) An Introduction to Molecular Neurobiology. Sinauer Associates Inc, Sunderland, Massachusetts.

    Google Scholar 

  9. Hartell NA (1996) Strong activation of parallel fibres produces localized calcium transients and a form of LTD that spreads to distant synapses. Neurons 16: 601–610.

    Article  Google Scholar 

  10. Holscher, C. (1997) Nitric oxide, the enigmatic neuronal messenger: its role in synaptic plasticity. Trends Neurosci. 20: 298–303.

    Article  Google Scholar 

  11. Husbands, P. (1998) Evolving Robot Behaviours with Diffusing Gas Networks, In: P. Husbands and J.-A. Meyer (1998), 71–86.

    Google Scholar 

  12. P. Husbands and J.-A. Meyer (Eds) (1998) EvoRobot98: Proceedings of 1st European Workshop on Evolutionary Robotics, Springer-Verlag LNCS 1468.

    Google Scholar 

  13. P. Husbands and T. Smith and N. Jakobi and M. O’Shea. Better Living through Chemistry: Evolving GasNets for Robot Control, Connection Science, 10(3&4), 185–210, 1998.

    Article  Google Scholar 

  14. Jakobi, N. (1998) Evolutionary Robotics and the Radical Envelope of Noise Hypothesis, Adaptive Behavior, 6(2): 325–368.

    Article  Google Scholar 

  15. Kandel, E. (1976) The cellular basis of behavior. Freeman.

    Google Scholar 

  16. Katz B (1969) The release of neural transmitter substances. Liverpool University Press.

    Google Scholar 

  17. Nolfi, S. and Floreano, D. (2000). Evolutionary Robotics: The biology, intelligence and technology of self-organizing machines. MIT Press.

    Google Scholar 

  18. Park J-H, Straub V and O’Shea M (1998) Anterograde signaling by Nitric Oxide: characterization and in vitro reconstitution of an identified nitrergic synapse. J Neurosci 18.

    Google Scholar 

  19. Philippedes, A. and P. Husbands and M. O’Shea. Four Dimensional Neuronal Signaling by Nitric Oxide: A Computational Analysis. Journal of Neuroscience 20(3): 1199–1207, 2000.

    Google Scholar 

  20. A. Philippedes and P. Husbands and T. Lovick and M. O’Shea (2001). Targeted gas clouds in the brain. (submitted)

    Google Scholar 

  21. T. Smith and P. Husbands and M. O’Shea (2001). Neutral Networks and Evovability with Complex genotype-Phenotype Mapping. Proc. ECAL’01. LNCS, Springer.

    Google Scholar 

  22. T. Smith and P. Husbands and M. O’Shea (2001). Not Measuring Evovability: Initial Investigations of an Evolutionary Robotics Search Space. In Proc. CEC’01, IEEE Press.

    Google Scholar 

  23. T. Smith and P. Husbands and M. O’Shea (2001). Evolvability, Neutrality and Search Difficulty. (submitted)

    Google Scholar 

  24. Wagner, G. and Altenberg, L. (1996). Complex adaptations and the evolution of evolvability. Evolution, 50(3):967–976.

    Article  Google Scholar 

  25. Wood J and Garthwaite J (1994) Model of the diffusional spread of nitric oxide-implications for neural nitric oxide signaling and its pharmacological properties. Neuropharmacology 33: 1235–1244.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Husbands, P., Philippides, A., Smith, T., O’Shea, M. (2001). The Shifting Network: Volume Signalling in Real and Robot Nervous Systems. In: Kelemen, J., Sosík, P. (eds) Advances in Artificial Life. ECAL 2001. Lecture Notes in Computer Science(), vol 2159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44811-X_3

Download citation

  • DOI: https://doi.org/10.1007/3-540-44811-X_3

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42567-0

  • Online ISBN: 978-3-540-44811-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics